The epidemiology of COVID-19 is poorly understood. To better understand the potential range of epidemic outcomes in the state of Georgia, we developed a model based on the accumulation of knowledge from various locations and calibrated it to regionally specific conditions in Georgia as well as observations of the number of reported cases, hospitalizations, and deaths.
This model supersedes prior stochastic models for Georgia published on this website.
At the time of this report, Georgia is reporting 23,399 cases, 3,965 hospitalizations, and 911 deaths. These data are from covidtracking.com. Grey bars represent reported data and the black line represents the fit of our model to observed data.
To date, the primary intervention has been the adoption of social distancing behaviors and improved hygiene. We measure social distancing using aggregated, anonymized locations recorded by location-based mobile phone apps. We summarize the effectiveness of social distancing behaviors as the average deviation from baseline, a statistic ranging from 0% (complete cessation of movements) to 100% (no difference from baseline).
We explore a range of social distancing scenarios in Georgia going forward from April 25, ranging from increasing social distancing to completely ending social distancing. For each scenario, we use our model to project the likely outcomes for each scenario. We run our model one thousand times for each scenario. The plot below summarizes the social distancing scenarios and the projected cumulative number of cases for each scenario, with a range of possible outcomes for each.
Here we explore a range of scenarios for transmission in Georgia. All simulations start on 1 March. The model is fit to data on reported cases, hospitalizations, and deaths to April 27, 2020 and propagated six weeks into the future.
Key dates for the control COVID-19 in Georgia include:
In what follows we consider six scenarios. The first four scenarios are projections of possible futures based on what has already happened in the past. The final two scenarios are simulations of the epidemic starting from March 1, 2020 showing the effectiveness of different interventions and behaviors. These two counterfactual scenarios explore what might have occurred if different actions had been taken in the past.
Possible futures scenarios
Effectiveness scenarios (past and future)
The following plot shows the range (80% of simulations fall in this range) of projected cumulative number of recorded cases, hospitalizations, and deaths six weeks from today for each scenario. The white dots indicate the median projection across simulations. Note that all three measures of epidemic size are believed to be under-reported, so the true epidemic size is likely to be considerably larger (e.g., the true number of cumulative infections is likely to be 10 to 12 times larger than the numbers reported in these figures).
Average of trajectories for each scenario.
The following plots show the observed and projected daily number of recorded cases, hospitalizations, and deaths for the six scenarios. Shaded regions represent the area within which 80% of the model simulations fall.
Key features of this model include:
This model comprises susceptible, pre-symptomatic, asymptomatic, symptomatic, diagnosed, hospitalized, deceased, and recovered persons. The following compartments are included:
To allow more realistic distributions of movement through compartments, several of these compartments are internally split into multiple stages using the linear chain trick.2
The flow diagram for this model shown below.
The following interventions are implemented:
This model was initially parameterized using clinical outcome reports from the epidemic in Hubei province, China and further calibrated with information about COVID-19 elsewhere in China and the United States.
The model is created for a population of 10.6 million people, approximately the population size of Georgia, and simulated forward from March 1. Transmissibility of the virus is assumed to be proportional to the level of human movement.
Key parameters estimated using maximum likelihood by iterated filtering (MIF)3 include baseline transmissibility (\(\beta_0\)), maximum ascertainment (i.e. maximum fraction of cases detected), fraction of known cases that are hospitalized, fatality rate among hospitalized cases, and observation errors. Auxiliary parameters estimated using MIF include the intensity of the parameter random walk and dispersion parameters for observables.
For more details, see the public GitHub repository
Disclaimer: The COVID-19 epidemic is changing rapidly, and information that was used in the construction of this model may be incomplete or contain errors. Accordingly, these results are preliminary, provisional, and subject to change. These results have not been peer-reviewed, but have been prepared to a professional standard with the intention of providing useful interpretation of a rapidly developing event.